a study on factors influencing low-carbon production behavior of...

International Journal of Contemporary Applied Researches Vol. 6, No. 4, April 2019

(ISSN:2308-1365) www.ijcar.net

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A Study on factors influencing low-carbon production behavior of industrial

enterprises in China

Xizhou Dai, Xin Meng, Shenrong Gao，Xia Tong*

Nantong University, School of Business, Nantong, Jiangsu, P. R. China 226001 *Corresponding author, E-mail address: [email protected]

Abstract

China industrial enterprises as the main low-carbon production, and its low-carbon production

is affected by energy consumption structure and industrial structure, the impact of such factors

as gross domestic product, the quality of education. Regression models to empirical analysis

of the factors related to, it can be concluded, energy consumption structure and industrial

structure, the gross domestic product of China's industrial enterprises has a positive impact on

carbon emissions, the quality of education has a negative effect on carbon emissions of

industrial enterprises in China. Through an analysis of these factors can be targeted to adopt

improvement measures for low-carbon production of industrial enterprises in China

Key words: industrial enterprises, low carbon production, regression models

mailto:[email protected]



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1 Introduction

The global climate situation is growing tense, with the Copenhagen climate agreement,

the Paris climate Protocol and other related agreements signed, people are increasingly aware

of the urgency to change the current climate situation. Therefore, mankind must improve the

current economic structure, energy saving and emission reduction, development of

low-carbon economy. The Low-carbon economy is an economic model of sustainable

development and, as yet, a proven way to improve the current climate, sustain economic

sustainability and respond to the various energy crises that may arise. As an industrial

enterprise with energy consumption and carbon emission, promoting its low-carbon

production will have an important impact on the future sustainable development. Therefore, it

is necessary to find out the main factors affecting the low carbon production of Chinese

industrial enterprises, in order to promote Chinese industrial enterprises to better low-carbon

production.

2 The literature review

Industrial enterprises are the main source of carbon emissions, control of industrial

enterprises carbon emissions is to achieve China's emission reduction targets in the most

important. At present, many scholars at home and abroad have studied deeply, and foreign

scholars focus on the market driving factors, the relationship between carbon emission and

economic growth and government regulation. Luken (2008) believes that market

characteristics have a greater impact on the cleaner and energy-efficient production practices

of enterprises in developing countries, and that the export of industrial enterprises has an

important impact on the willingness and behavior of their low-carbon production. However,

Sarumpaet (2005) Studies of Indonesian enterprises have concluded that the export of

products is not significantly related to the use of environmentally-friendly production

methods. Eban Goodstein (2002) uses the Lmdi factor decomposition method to obtain the

positive correlation between the economic growth and the carbon emission change. Montalvo

(2008) found that the government's compulsory system arrangement is an important driving

force for enterprises to adopt low-carbon related production behavior. Domestic scholars have

less relevant research, which mainly involves the guidance of national policy, the influence of

technological change and the drive of market economy, which affect the Low-carbon

production behavior of industrial enterprises. Gao Liangmou and Tan Yu (2008) think that

exerting the government's leading role is of great significance to improve the enterprise's low

carbon production intention. Su Lihua (2011) believes that only the government has the ability

to organize and promote low-carbon economic development. Liu, Yin Xiangdong (2011) In



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the study found that a wide range of market prospects will encourage industrial enterprises to

be more willing to do low carbon production. Yu Guopu, welcome, Cao Jianping. (2012) This

paper expounds the influence of low carbon production technology on the low carbon

production of industrial enterprises. Wang (2014) believes that higher technological levels in

the industrial sector will help reduce industrial carbon emissions. Juan (2016) that the low

carbon technology directly determines the various input resources can achieve low carbon

conversion efficiency.

3 Analysis on the current situation and influencing factors of carbon emission in Chinese

industrial enterprises

Combined with "China Statistical Yearbook 2016" in the various industries Energy

balance table, the carbon emissions from coal, coke, crude oil, gasoline, kerosene, diesel, fuel

oil, liquefied petroleum gas and natural gas 9 fossil fuels are calculated synthetically, and the

data from the IPCC 2006 National Greenhouse Gas Inventories guide is selected to calculate

carbon emissions ( As shown in table 1, units: ton of carbon/ton coal) Get the accounting

formula:iii BAE

9

1i

C

Among them: I=1,......, 9, indicating energy type, EI for energy consumption total (unit:

million tons), AI for each fossil energy of the folding coal coefficient, bi is the carbon emission

factor of standard coal. A variety of energy-folding coal coefficient and the carbon emission

factor after the dismantling of the coal are shown in table 1.

Table1 Carbon emission coefficients for various energy sources

The sources of

energy Coefficient of marking coal Carbon emission factor

Coal 0.7134

0.7476

Coke 0.9714

0.8388

Crude oil 1.4286

0.8363

Gasoline 1.4714 0.814

Kerosene 1.4714 0.8442

Diesel oil 1.4571 0.8616

Fuel oil 1.4286 0.8223



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Liquefied

Petroleum Gas

1.7143 0.8631

Natural gas 1.33 0.5956

According to the formula, the specific values of China's industrial carbon emissions are

obtained, as shown in Figure 1 (units: million tons)

Fig.1 China's industrial carbon emissions 1996-2015

The low carbon production behavior of Chinese industrial enterprises is influenced by many

factors, Lin Wei-ming and Yu Jianhui (2014) are analyzed from three aspects of government

environment, consumer and enterprise characteristics, and this article analyses from four

aspects of energy consumption structure, industrial structure, GDP and education quality

according to the research practice.

3.1 Energy consumption structure

Reform and opening-up in the past more than 30 years, China has initially formed a

comprehensive energy supply pattern with coal as its main body, electric power as the center,

oil and natural gas and renewable energy to develop comprehensively, and has basically

established a comparatively perfect supply system. As can be seen from table 2, China's energy

consumption has changed dramatically during the 20 years of 1996-2015. Coal, as a major

source of carbon emissions, has always been the dominant energy consumption market in China.

However, with the development of Low-carbon technology and new energy in recent years, the

proportion of coal in energy consumption is also declining, oil energy has been standing still,

while the share of other energy sources represented by electricity and natural gas has been

101664.6752

148110.2593

212716.2109

245398.5049

0

50000

100000

150000

200000

250000

300000

1995 2000 2005 2010 2015 2020



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rising, which shows good prospects for clean energy. The change of this energy consumption

structure has promoted the Low-carbon production of Chinese industrial enterprises.

Fig. 2 Main energy consumption in China (%)

3.2Industrial structure

Industrial structure will not only have a significant impact on economic growth, but also the

decisive factor of energy consumption and carbon emission. Therefore, the change of industrial

structure is a factor that can not be neglected in the process of studying the main factors

affecting the low carbon production behavior of Chinese industrial enterprises.

Fig. 3 China industrial Structure

0102030405060708090

100

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Coal Oil Natural gas Primary electricity and other energy

0

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15value-added of the primary industry(%)

value-added of the secondary industry（%）

value-added of the tertiary industry（%）



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As shown in Fig. 3, the proportion of primary GDP is declining, the second industry has a

relatively stable situation in the last 20 years, and the tertiary sector has a general rise in GDP.

Because industry is the main cause of carbon dioxide emissions, so China's energy consumption,

high pollution of the economic model occupy "half of the" second industry is the main cause of

China's carbon emissions.

3.3Gross domestic product

Since 1997 especially into the 21st century, China's total economic growth in global GDP

ranked across the rise. Overtook Italy at the end of 2005, ranked sixth in the world, surpassed

the UK after 1 years, fourth in 2007, and overtook Japan in 2010 to become the third in the

world. From the 20 years of 1996-2015, China's total economic growth from 1996 to

68,905,210,000,000 Yuan in 2015, the increase of more than 9.6 times times, China's economy

in a rapid development era, creating a world-famous "Chinese-style economic miracle."

Fig. 4 China's GDP and GDP per capita (1996-2015)（unit：yuan）

3.4The quality of education continues to rise

After 977 years of resuming the college entrance examination, China has been increasing

the investment in education. In the past 20 years, China's education quality and teaching scale

has been rising, whether it is the general undergraduate enrollment number or graduate

enrollment number have made great strides. The overall level of education in a country often

means a country's future trends. Since 1996, the general undergraduate college and graduate

enrollment has undergone a leap-forward development.

0

100000

200000

300000

400000

500000

600000

700000

800000

19

96

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Per capita GDP GDP



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Fig. 5 The number of college enrollment and postgraduate enrollment in China

(1996-2015)

4 Variable selection and model construction

4.1 Variable selection and data source

Choose China Industrial enterprise Carbon emission growth rate (ΔY) as the explanatory

variable, energy structure (X1), industrial structure (ΔX2), educational aspect set up the

average rate of enrollment growth (ΔX3) and the GDP index to select per capita GDP

(ΔMGDP) and gross domestic product (ΔGDP) As an explanatory variable for interpreting

dependent variables. This article selects 1997-2015 Year's sample data, the related data comes

from the National Bureau of Statistics Network, the concrete sample value is as follows (the

unit is%):

Table 2 Sample Data sheet for nearly 19 years

Year Y 1X 2X 3X MGDP GDP

1997 5.31 71.40 47.20 3.52 9.88 11.00

1998 -3.91 70.90 46.00 8.40 5.85 6.88

1999 0.20 70.60 45.50 42.86 5.38 6.30

2000 -17.04 68.50 45.60 42.46 9.86 10.73

2001 7.67 68.00 45.00 21.61 9.76 10.55

2002 5.36 68.50 44.60 19.46 9.05 9.79

2003 18.71 70.20 45.80 19.25 12.20 12.90

2004 14.99 70.20 46.00 17.03 17.07 17.77

2005 17.79 72.40 47.20 12.78 15.06 15.74

2006 6.46 72.40 47.70 8.25 16.49 17.15

0

100

200

300

400

500

600

700

800

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

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06

20

07

20

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20

09

20

10

20

11

20

12

20

13

20

14

20

15

ordinary undergraducate enrollment graduate enrollment



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2007 6.24 72.50 46.80 3.63 22.51 23.15

2008 3.69 71.50 47.10 7.38 17.63 18.24

2009 4.88 71.60 46.10 5.24 8.71 9.25

2010 16.05 69.20 46.60 3.48 17.75 18.32

2011 0.62 70.20 46.60 2.98 17.90 18.47

2012 16.71 68.50 45.50 1.08 9.90 10.44

2013 4.81 67.40 44.30 1.60 9.61 10.90

2014 -2.03 65.60 43.30 3.08 7.64 7.46

2015 -3.74 64.00 41.10 2.28 6.46 7.00

4.2 Basic statistical description of variables

The basic statistical description of all variables, the overall development of the variable

description, which will be the main reference to the difference between the mean and the

maximum, the specific situation is as follows (%):

Table 3 Basic statistical Description of variables

Variab

le Y 1X

2X 3X MGDP GDP

Averag

e 5.41 69.66 45.68 11.91 12.04 12.74

Median 5.31 70.20 46.00 7.38 9.88 10.90

Maxim

um 18.71 72.50 47.70 42.86 22.51 23.15

Minimu

m 17.04 64.00 41.10 1.08 5.38 6.30

Value

differen

ce

35.75 8.50 6.60 41.78 17.13 16.85

Sample

size 19 19 19 19 19 19

A statistical description of the results from table 3 shows that the average growth rate of

industrial carbon emissions in 19 is maintained at 5.41%, as a whole, it shows that the volume

of carbon displacement is larger, the maximum value of the growth rate is different, and the

growth rate is in negative state in a year. Shows that the current policy of carbon emissions

has a strong limit to deal with; From energy structure, the results of industrial structure,

education and GDP show that the average growth rate of the whole is positive, indicating that

all of them are in a certain growth trend, and the difference between the energy structure and

the industrial structure is relatively small, which indicates that the growth rate of the two is

relatively flat over time.



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4.3 Setting of the model

The setting of the model, with a number of methods and approaches, combined with the

variable characteristics of this paper, the establishment of industrial enterprise carbon emission

growth rate (ΔY) and coal production accounted for the proportion of energy consumption (X1),

secondary growth rate (ΔX2), the average number of college enrollment rate (ΔX3), per capita

GDP (ΔMGDP) and the linear model of gross domestic product (ΔGDP). This is based on the

development of variables between the trends in the direction of a similar feature to determine

the number of variables between the development relationship.

The specific model is: 0 1 1 2 2 3 3 4 5t t t t t t tY X X X MGDP GDP （1）

Where β0 is a constant term, βn,n=1 ... 5, the explanatory factor of the variable to the

dependent variable, the εt is the random error term.

5 Empirical analysis

5.1 Test of variable smoothness

In this paper, the ADF unit root test method is used to test the variables before the data are

processed.

Table4 ADF unit root test output

Varia

ble

Null

hypothesis Lag order

The value of

ADF

P-Stati

stic

Whether it is

stable

Y unit root

exists, the

sequence is

not stable

0 -1.258 0.183

P>0.05not pass

the test, it is not

stable

1 -7.802 0.000 P<0.05pass the

test, it is stable

1X

unit root

exists, the

sequence is

not stable

0 -1.363 0.154

P>0.05 not pass

the test, it is not

stable

1 -0.993 0.274

P>0.05 not pass

the test, it is not

stable

2 -9.505 0.000 P<0.05 pass the

test, it is stable

2X unit root

exists, the 0 -1.567 0.108

P>0.05 not pass

the test, it is not



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sequence is

not stable

stable

1 -2.230 0.029 P<0.05 pass the

test, it is stable

3X unit root

exists, the

sequence is

not stable

0 -2.258 0.196

P>0.05 not pass

the test, it is not

stable

1 -3.513 0.002 P<0.05 pass the

test, it is stable

MGDP

unit root

exists, the

sequence is

not stable

0 -0.490 0.487

P>0.05，not pass

the test, it is not

stable

1 -7.411 0.000 P<0.05 pass the

test, it is stable

GDP

unit root

exists, the

sequence is

not stable

0 -1.984 0.290

P>0.05，not pass

the test, it is not

stable

1 -5.335 0.001

P<0.05refuse the

null hypothesis, it

is not stable

From the hypothesis of table 4, we can see that the variable itself does not pass the ADF test

under the 0.05 significant level on the basis of the original hypothesis that the sequence has unit

root and the sequence is non-stationary. In addition to the stability of variable X1 in the

two-phase lag, the remaining variables are stable after a period of lag. In order to further study

the long-term stability of variables, the cointegration test is carried out to determine the

long-term cointegration effect and causality between variables.

5.2Cointegration test

From the stationary test, the variable itself is non-stationary, but has the same order

(1-order) stationary characteristics. In this paper, cointegration test can be used to study the

quantitative relationship between nonstationary variables, and whether the causal relationship

described by regression equation is pseudo regression, that is, whether there is a stable



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relationship between variables. According to the principle of cointegration test, it is found that

the smoothness of residuals is the precondition of determining whether the equation has a

long-term stable relationship. So this section uses the least square method to carry out the

cointegration test, and puts forward the original hypothesis: the residual unit root is 0, that is,

there is no co-integration relationship.

First of all, the regression equation is obtained by using least square method for the

dependent variable and the independent variable, and the regression equations are as follows:

1 1 2 33.758 2.328 0.185 1.114 0.765t t t t t tY X X X MGDP GDP （2）

（-0.722）（0.162）（0.187）（-0.996）（0.139）（-0.094）

2R =0.563 F=2.336 D.W=0.033

Secondly, the stability of the residual error

^

t caused by regression is tested.

Table 5 Residual item ADF unit root test

Variable Lag order Test value

of ADF

Incidental

probability

P

Pass

situation of

the null

hypothesis

Whether it

is stable

^

t 0 -4.0375*** 0.0004

P<0.05，

refuse the

null

hypothesis

stable

Annotation："***"means passing the significance level test of 0.01,using Eviews7.0 software.

From the results of the ADF unit root test in table 5, we can see that the residual items pass

the ADF unit root, which has the characteristics of smoothness, so it can be further explained

that there is a long-term stable equilibrium relationship between two variables.

5.3Correlation test

The independent variable introduced in this paper is the expansion of a single target, and in

the regression results of model (2), the model has a poor fitting effect and the variables are not

validated by the corresponding significance, so the model variable has a large deviation. In the

explanatory model of the dependent variable, the multiple collinearity is often present, which

will cause error to the accuracy of the regression model. In this part, the correlation test is used

to determine the collinearity characteristics of the variables. The general correlation coefficient

which is more than 0.8 has a very strong correlation, the coefficient of 0.6-0.8 means variables



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have a relatively strong correlation, and the coefficient which is less than 0.6 indicates that there

is a weak correlation between the variables.

Table 6Correlation test

Y 1X

2X 3X MGDP GDP

Y 1.000

0.000

0.000

1X 0.608 1.000

1.335 0.000

0.199 0.000

2X 0.638 0.928 1.000

1.481 10.252 0.000

0.157 0.000 0.000

3X -0.613 0.055 0.018 1.000

-1.361 0.229 0.076 0.000

0.191 0.822 0.940 0.000

MGDP

0.704 0.517 0.507 -0.278 1.000

1.823 2.488 3.149 -1.192 0.000

0.086 0.024 0.006 0.250 0.000

GDP

0.704 0.537 0.526 -0.264 0.998 1.000

1.818 2.625 3.306 -1.127 68.100 0.000

0.087 0.018 0.004 0.275 0.000 0.000

Annotation：The first row is correlation coefficient. The second row is the value of t. The third

line is incidental probability.

It can be seen from the results of correlation test in table 6 that there is a strong correlation

between the dependent variable and the independent variable.At the same time, the variable 1X

has a strong correlation with the variable 2X and MGDP has a strong correlation with

GDP . So variables have related characteristics.

In order to reduce the error caused by multi-collinearity, this paper adopts the stepwise

regression method, gradually increasing the variables based on the basic model, and judges the



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final optimal model according to the size of the determinable coefficient.So the fitting effect of

the simulation is optimal.

Table 7 Optimal model selection result

Model 1( 1)X 2X

Analyzin

g

condition

s

coefficient 3.758** 5.886*

* =0.563

1 1 2 2( 1)t t tY X X

the value of t 2.328 2.376 F=2.694

Incidental

probability 0.033 0.030 P=0.012

Annotation："**"means passing the significance level test of 0.05.(-1)means the lag one of the

variable.

From the table 7, by judging the condition, establishing 2X as the regression of the

independent variable to the dependent variable makes the model fitting effect superior.

Therefore, the interpretation variable1( 1)X reflecting carbon emissions is more ideal by

choosing energy structure and industrial structure. The specific explanation equation is:

1 1 23.758 5.886t t tY X X

（2.328）（2.376）

2R =0.563 F=2.694 D.W=0.012

5.4Empirical results and analysis

The statistic is tested, and the above model can increase the value of the coefficient of

determination , which shows that the overall regression effect of the model is relatively good.

The energy structure variable and the second industry change rate variable can be

quantitatively explained by the influence of equation coefficient on industrial carbon

emissions according to univariate significance test.

(1)In the energy structure, the proportion of coal accounting for energy consumption has a

lagging effect,which is also has a positive lag effect with the dependent variable. That is, every

time 1 unit is increased in the change rate of the proportion of coal accounting for energy

consumption, the growth rate of carbon emissions will be increased by 3.758 unit in the next

period.

(2)In the industrial structure, the change rate of the total value of the secondary industry has



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passed the significance test, and has a positive contemporaneous effect with the dependent

variable. That is, the growth rate of the unit second output value will increase, which will

increase the growth rate of industrial carbon emissions by 5.886unit. Its impact is greater than

the energy structure.

(3)In the educational structure, the number of students in the general undergraduate has

continued to rise and has a reverse effect with the dependent variable. That is, the number of

students in the general undergraduate has risen, the growth rate of industrial carbon emissions

has declined, and the relative industrial and energy structure are not obvious.

(4)In the national economic structure, the growth of GDP and per capita GDP has a positive

effect on carbon emissions.The growth rate of industrial carbon emissions has increased

significantly during the period of rapid growth of the national economy. After the rapid growth,

and the industrial carbon emission rate has dropped significantly in the slow rise period.

6 Countermeasure and suggestion

Through the empirical analysis of the main factors that influence the production behavior

of low carbon production in Chinese industrial enterprises, the paper sums up the correlation

and influence degree of several major factors. Based on the above results, five points are put

forward to improve the low-carbon production of Chinese industrial enterprises.

(a) Improve the structure of economic development and promote the upgrading and transferring

of industrial structure

At the present stage of China's economic development model, "High energy consumption,

high pollution environment" is still the main characteristics of the second industry. Therefore,

we should guide the development of the tertiary industry and gradually promote the tertiary

industry to occupy the leading position in economic growth. At the same time, we should use

science and technology to drive the primary and the secondary industries’technological

innovation and use the technology products in high-tech industries to promote the

transformation in the industry with high energy consumption and pollution . On the industrial

transfer, we can start from two angles. From China's point of view, we should coordinate the

development of various industries in the region and improve the situation of China's imbalance

in the east and the west . From a global point of view, these industries can be gradually

transferred to the developing countries where the human cost is low andthe environmental

protection is insufficient.

(b) Strengthen national policy leadership to promote low-carbon consumer market

development

At present,with the promotion of the government, media, enterprises and social non-profit



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groups, the concept and awareness of low-carbon consumption begin to become more popular.

Therefore, the Chinese government must be committed to building and establishing the right

low-carbon consumption concept, guiding reasonably the production and consumption of

enterprises and residents, encouraging the relevant product input, research and development

and production. The country should promote the society to be accustomed to low carbon

consumption, to form a kind of environmental protection, health and civilized low-carbon

consumption.

(c) Increase education input and focus on low-carbon concept training

Education is a plan for the national quality and the long-term development of the country.

While the development of low-carbon economy and the road to sustainable development are

the country's long-term development of the necessary direction. Therefore, the state should

increase the financial investment in education, provide sufficient funds for upgrading

hardware facilities, improve the level of teachers, and update the concept of running school to

meet international education standards. At the same time, in the education process, we should

pay more attention to the concept of sustainable development and publicize the low carbon

economy to babies,which is throughout their entire learning careers.

（d）Strengthen publicity

China's huge population base of more than one billion shows that energy conservation and

emission reduction cannot rely solely on macro-level leadership and regulation at the

government level. At the micro level, it is necessary to raise the environmental awareness of all

citizens. While the government regulates the industrial structure and energy structure, it must

also pay attention to the propaganda work of energy conservation and emission reduction

measures within the social scope. Only when everyone pays attention to saving energy and

reducing greenhouse gas emissions in their daily lives can they effectively reduce carbon

emissions in their daily lives.

(e)Improve the existing judicial system and strictly control violations

China's current legislation on environmental protection is light.Many environmental laws

was made too early and the punishment is insufficient. As China's economy has advanced, the

illegal cost has become an inevitable and widespread question. Therefore, the government

should focus on improving the judicial and legislative systems related to the environment,

upgrading existing environmental standards, achieving international standards, increasing

penalties, and being fair and open.



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